





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_snake_case    :       Optional[int]		       =    logging.getLogger(__name__)
_snake_case    :       Dict		       =    list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_snake_case    :       List[Any]		       =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 							_UpperCAmelCase       :

  """simple docstring"""


  a_              =					field(
      default=_UpperCamelCase						,       metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
          )
      }						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCamelCase       )}						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={
          """help""": (
              """Override some existing default config settings when a model is trained from scratch. Example: """
              """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
          )
      }						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}						,       )
  a_              =					field(
      default="""main"""						,       metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }						,       )






  def 							lowercase						(							self					:						List[Any]	)				->		List[Any]:
     if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
        raise ValueError(
            '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'	)





@dataclass
class 							_UpperCAmelCase       :

  """simple docstring"""


  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}       )
  a_              =					field(default=_UpperCamelCase						,       metadata={"""help""": """The input training data file (a text file)."""}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )
  a_              =					field(
      default=5						,       metadata={
          """help""": """The percentage of the train set used as validation set in case there's no validation split"""
      }						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated. Default to the max input length of the model."""
          )
      }						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """The number of processes to use for the preprocessing."""}						,       )
  a_              =					field(
      default=0.15						,       metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={
          """help""": (
              """Whether to pad all samples to `max_seq_length`. """
              """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
          )
      }						,       )






  def 							lowercase						(							self					:						int	)				->		int:
     if self.train_file is not None:
        __lowerCAmelCase				      =      self.train_file.split('.'	)[-1]
        assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
     if self.validation_file is not None:
        __lowerCAmelCase				      =      self.validation_file.split('.'	)[-1]
        assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def     a_       (					lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     Union[str, Any]      ):
   with open(lowerCAmelCase_,      'r',      encoding='utf-8'      ) as f:
      __lowerCAmelCase				      =      [json.loads(lowerCAmelCase_      ) for line in f.read().splitlines() if (len(lowerCAmelCase_      ) > 0 and not line.isspace())]
   assert len(lowerCAmelCase_      ) == len(lowerCAmelCase_      )

   __lowerCAmelCase				      =      {c: dataset[c] for c in dataset.column_names}
   __lowerCAmelCase				      =      refs
   return Dataset.from_dict(lowerCAmelCase_      )

def     a_       (					):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   __lowerCAmelCase				      =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
   if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
   else:
      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   __lowerCAmelCase				      =      None
   if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
      __lowerCAmelCase				      =      get_last_checkpoint(training_args.output_dir      )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
         raise ValueError(
             F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             'Use --overwrite_output_dir to overcome.'      )
      elif last_checkpoint is not None:
         logger.info(
             F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'      )

    # Setup logging
   logging.basicConfig(
       format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',      datefmt='%m/%d/%Y %H:%M:%S',      handlers=[logging.StreamHandler(sys.stdout      )],      )
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank      ) else logging.WARN      )

   # Log on each process the small summary:
   logger.warning(
       F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + F"""distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}"""      )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank      ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('Training/evaluation parameters %s',      lowerCAmelCase_      )

   # Set seed before initializing model.
   set_seed(training_args.seed      )

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      __lowerCAmelCase				      =      load_dataset(data_args.dataset_name,      data_args.dataset_config_name      )
      if "validation" not in datasets.keys():
         __lowerCAmelCase				      =      load_dataset(
             data_args.dataset_name,      data_args.dataset_config_name,      split=F"""train[:{data_args.validation_split_percentage}%]""",      )
         __lowerCAmelCase				      =      load_dataset(
             data_args.dataset_name,      data_args.dataset_config_name,      split=F"""train[{data_args.validation_split_percentage}%:]""",      )
   else:
      __lowerCAmelCase				      =      {}
      if data_args.train_file is not None:
         __lowerCAmelCase				      =      data_args.train_file
      if data_args.validation_file is not None:
         __lowerCAmelCase				      =      data_args.validation_file
      __lowerCAmelCase				      =      data_args.train_file.split('.'      )[-1]
      if extension == "txt":
         __lowerCAmelCase				      =      'text'
      __lowerCAmelCase				      =      load_dataset(lowerCAmelCase_,      data_files=lowerCAmelCase_      )
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   __lowerCAmelCase				      =      {
       'cache_dir': model_args.cache_dir,
       'revision': model_args.model_revision,
       'use_auth_token': True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      __lowerCAmelCase				      =      AutoConfig.from_pretrained(model_args.config_name,      **lowerCAmelCase_      )
   elif model_args.model_name_or_path:
      __lowerCAmelCase				      =      AutoConfig.from_pretrained(model_args.model_name_or_path,      **lowerCAmelCase_      )
   else:
      __lowerCAmelCase				      =      CONFIG_MAPPING[model_args.model_type]()
      logger.warning('You are instantiating a new config instance from scratch.'      )
      if model_args.config_overrides is not None:
         logger.info(F"""Overriding config: {model_args.config_overrides}"""      )
         config.update_from_string(model_args.config_overrides      )
         logger.info(F"""New config: {config}"""      )

   __lowerCAmelCase				      =      {
       'cache_dir': model_args.cache_dir,
       'use_fast': model_args.use_fast_tokenizer,
       'revision': model_args.model_revision,
       'use_auth_token': True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      __lowerCAmelCase				      =      AutoTokenizer.from_pretrained(model_args.tokenizer_name,      **lowerCAmelCase_      )
   elif model_args.model_name_or_path:
      __lowerCAmelCase				      =      AutoTokenizer.from_pretrained(model_args.model_name_or_path,      **lowerCAmelCase_      )
   else:
      raise ValueError(
          'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
          'You can do it from another script, save it, and load it from here, using --tokenizer_name.'      )

   if model_args.model_name_or_path:
      __lowerCAmelCase				      =      AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path,      from_tf=bool('.ckpt' in model_args.model_name_or_path      ),      config=lowerCAmelCase_,      cache_dir=model_args.cache_dir,      revision=model_args.model_revision,      use_auth_token=True if model_args.use_auth_token else None,      )
   else:
      logger.info('Training new model from scratch'      )
      __lowerCAmelCase				      =      AutoModelForMaskedLM.from_config(lowerCAmelCase_      )

   model.resize_token_embeddings(len(lowerCAmelCase_      )      )

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      __lowerCAmelCase				      =      datasets['train'].column_names
   else:
      __lowerCAmelCase				      =      datasets['validation'].column_names
   __lowerCAmelCase				      =      'text' if 'text' in column_names else column_names[0]

   __lowerCAmelCase				      =      'max_length' if data_args.pad_to_max_length else False

   def tokenize_function(lowerCAmelCase_ :     str      ):
      # Remove empty lines
      __lowerCAmelCase				      =      [line for line in examples['text'] if len(lowerCAmelCase_      ) > 0 and not line.isspace()]
      return tokenizer(examples['text'],      padding=lowerCAmelCase_,      truncation=lowerCAmelCase_,      max_length=data_args.max_seq_length      )

   __lowerCAmelCase				      =      datasets.map(
       lowerCAmelCase_,      batched=lowerCAmelCase_,      num_proc=data_args.preprocessing_num_workers,      remove_columns=[text_column_name],      load_from_cache_file=not data_args.overwrite_cache,      )

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      __lowerCAmelCase				      =      add_chinese_references(tokenized_datasets['train'],      data_args.train_ref_file      )
   if data_args.validation_ref_file is not None:
      __lowerCAmelCase				      =      add_chinese_references(
          tokenized_datasets['validation'],      data_args.validation_ref_file      )
   # If we have ref files, need to avoid it removed by trainer
   __lowerCAmelCase				      =      data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      __lowerCAmelCase				      =      False

   # Data collator
   # This one will take care of randomly masking the tokens.
   __lowerCAmelCase				      =      DataCollatorForWholeWordMask(tokenizer=lowerCAmelCase_,      mlm_probability=data_args.mlm_probability      )

   # Initialize our Trainer
   __lowerCAmelCase				      =      Trainer(
       model=lowerCAmelCase_,      args=lowerCAmelCase_,      train_dataset=tokenized_datasets['train'] if training_args.do_train else None,      eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None,      tokenizer=lowerCAmelCase_,      data_collator=lowerCAmelCase_,      )

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         __lowerCAmelCase				      =      last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
         __lowerCAmelCase				      =      model_args.model_name_or_path
      else:
         __lowerCAmelCase				      =      None
      __lowerCAmelCase				      =      trainer.train(resume_from_checkpoint=lowerCAmelCase_      )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      __lowerCAmelCase				      =      os.path.join(training_args.output_dir,      'train_results.txt'      )
      if trainer.is_world_process_zero():
         with open(lowerCAmelCase_,      'w'      ) as writer:
            logger.info('***** Train results *****'      )
            for key, value in sorted(train_result.metrics.items()      ):
               logger.info(F"""  {key} = {value}"""      )
               writer.write(F"""{key} = {value}\n"""      )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir,      'trainer_state.json'      )      )

    # Evaluation
   __lowerCAmelCase				      =      {}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'      )

      __lowerCAmelCase				      =      trainer.evaluate()

      __lowerCAmelCase				      =      math.exp(eval_output['eval_loss']      )
      __lowerCAmelCase				      =      perplexity

      __lowerCAmelCase				      =      os.path.join(training_args.output_dir,      'eval_results_mlm_wwm.txt'      )
      if trainer.is_world_process_zero():
         with open(lowerCAmelCase_,      'w'      ) as writer:
            logger.info('***** Eval results *****'      )
            for key, value in sorted(results.items()      ):
               logger.info(F"""  {key} = {value}"""      )
               writer.write(F"""{key} = {value}\n"""      )

   return results

def     a_       (					lowerCAmelCase_ :     Tuple      ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()








"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'visual_bert'





      def __init__(			self ,		lowercase=30_522 ,		lowercase=768 ,		lowercase=512 ,		lowercase=12 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu" ,		lowercase=0.1 ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=2 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			visual_embedding_dim
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			num_attention_heads
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			attention_probs_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			bypass_transformer
            lowerCAmelCase    		=			special_visual_initialize






